MissingValuePattern
is an option for SynthesizeMissingValues and ToTabular to specify which data elements are considered missing.
Details
- MissingValuePattern is typically used to identify missing values. For instance, it is common to have some proxy value be interpreted as missing.
- Possible settings include:
-
Automatic automatically determine missing values patt use patt for each element to determine missing values {colipatti,coljpattj,…} use patti to determine missing values in column coli {patt1,…,pattn} use patti to determine missing values in column i - The patti can also be Automatic, in which case, missing for that column is automatically recognized.
- By default, Missing[…] and Indeterminate values are considered missing.
Examples
open allclose allBasic Examples (2)
Scope (4)
Specify missing values with Condition:
Train a distribution on a two-dimensional dataset:
Specify that missing values are indicated by the value "7":
By default, Tabular only interprets explicit Missing[…] expressions as missing values:
Use ToTabular to specify a pattern for the entries that should be additionally interpreted as missing values:
Specify multiple MissingValuePattern values:
Applications (1)
Train a distribution on the images:
Use MissingValuePattern to replace the pixel values that should be considered missing with the samples generated from the learned distribution:
Text
Wolfram Research (2019), MissingValuePattern, Wolfram Language function, https://reference.wolfram.com/language/ref/MissingValuePattern.html (updated 2025).
CMS
Wolfram Language. 2019. "MissingValuePattern." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2025. https://reference.wolfram.com/language/ref/MissingValuePattern.html.
APA
Wolfram Language. (2019). MissingValuePattern. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/MissingValuePattern.html